Feedback Loop

A feedback loop is system structure that causes
output from one node to eventually influence input to that same node.

For example, the work output of a population can increase the goods
and services available to that population, which can increase
the average life expectancy, which can increase the population,
which can increase the work output still more, and the loop
starts all over again. Using system
dynamics notation, this feedback loop would look like
the Population Growth loop shown.

The Voter Feedback Loop

A critical example of a feedback loop is the Voter Feedback Loop. This loop is the foundation of modern democracy.

You may have noticed that democracy is in crisis. It's unable to solve important common good problems like war, climate change, discrimination, and excessive income inequality. It's also unable to solve the hate-based authoritarianism problem, as seen in the recent rise to ascendance of Vladamir Putin and Donald Trump.

Why is that?

One of the intermediate causes is that the Voter Feedback Loop is in trouble. Thus this loop must be understood if we are to become able to dig deeper and uncover the root causes of why democracy is in crisis.

Here's how the loop works: When citizens decide who to vote for, they compare desired politician performance to actual politician performance. The difference is the politician performance gap. If the gap is low it hardly matters who to vote for. Otherwise, people vote for the "best" candidate to close the gap. Politicians know voters think this way, which causes a politician incentive to please voters by optimizing the common good. By standing for the common good, politicians can attract a majority of voters and win elections. Once elected, politicians engage in actual politician performance and the loop starts all over again.

The Voter Feedback Loop is a balancing loop because the goal of the loop "balances" the behavior of voters to slowly but surely drive the behavior of politicians to be the same as the goal of the loop, as the gap gets smaller and smaller. Balancing loops are also called goal-seeking loops. Like the way the setting of a room thermostat drives a heating and cooling system to the preferred temperature of the room, the goal of the Voter Feedback Loop drives a democratic system to the preferred state of "good" performance of its politicians.

The Voter Feedback Loop contains enormous power, since it's the fundamental driver of democratic governments. But it's far from mature and easily exploited. Powerful root cause forces are working to weaken the loop, as the annotated version explains below.

Note the final conclusion:

"When winning by telling lies instead of the truth becomes the norm, democracy is in crisis."

Why feedback loops are an important tool

While the Population Growth example may seem simple and obvious, that's because
we drew a simple model with only one loop and 4 variables.
Few real world problems are as simple. For example, The Dueling
Loops of the Political Powerplace model has 43 variables, 3 main
loops, about 5 additional loops, and 4 stocks. It is a simplification
of a larger model that has 123 variables, 11 stocks, and countless
loops. Its construction took a single person several years.

Another
example is the World3 model in Limits to Growth, which
contains about 300 variables and 20 stocks. It took 17 researchers
2 years to construct the model.

The Voter Feedback Loop is a simple loop for educational purposes. Much more detail would be needed, in a simulation model, to deeply and correctly determine how the loop works and how it's being weakened by those seeking to subvert the democratic process for their own gain.

Understanding the behavior of difficult complex social system
problems well enough to even begin to hypothesize a realistic solution,
with a high probability of working the first time, is impossible
without understanding the key feedback loops involved.

A quick introduction to how feedback loops work

The universe contains only two kinds of feedback loops: reinforcing and balancing, also called positive and negative feedback loops. Once you grasp how they work you are well on your way to understanding the foundation of systems thinking.

A feedback loop occurs when a change in something ultimately comes back to cause a further change in the same thing. If the further change is in the same direction it’s a positive or reinforcing loop. If it’s in the opposite direction it’s a negative or balancing loop, also called a goal-seeking loop.

An example of a reinforcing loop is Population Growth. As population goes up, so does births per year. As that goes up, so does future population. The loop goes round and round, growing exponentially until the loop hits its limits, which are not shown.

An example of a balancing loop is Constrained Population Growth. Here the constraint is carrying capacity, which is the maximum number of people a system can support. Population will grow until it reaches this constraint, also known as a limit or target. Let's call this one a limit.

In a balancing loop the gap equals the limit minus the actual state. Suppose the carrying capacity is 100 people and population starts at 10. That causes a population gap of 90. This increases births per year to a high rate. As that goes up, so does population. As population rises, the population gap falls. This lowers the birth rate, which (over a long period of time) lowers population, which increases the gap, which increases the birth rate, and so on until the gap approaches zero. This behavior causes population to gradually approach the carrying capacity of the system, since the system can support a limited number of people. In practice population will tend to overshoot carrying capacity and suddenly collapse, due to long delays in environmental degradation.

Every time a balancing loop goes around, it behaves in the opposite manner than it did before, since a balancing loop contains an odd number of inverse relationships. A reinforcing loop contains an even number of inverse relationships. A solid arrow indicates a direct relationship. A dashed arrow is an inverse relationship. Understanding how balancing loops behave can be tricky at first. If so, do what we did. Draw a few reinforcing and balancing loops on paper. Trace them around until things start to become clear.

Another example of a balancing loop is a Thermostat. Suppose you set the target temperature to 65 degrees. The higher the target the greater the temperature gap. The greater the gap the more heat that flows into the system. That increases the temperature. As this goes up the temperature gap goes down. It keeps going down until the gap is zero, at which point the system has reached the target.

These are causal loop diagrams. They can't be simulated but are very useful for simple or high level feedback loop modeling.

Arrows indicate that one node influences another. Solid arrows are a direct relationship. One node varies directly with another. If A goes up then so does B, or if A goes down then so does B. Dashed arrows are an inverse relationship. If A goes up then B goes down and vice versa. As simple as models like these are, they can allow problem solvers to understand the relevant behavior of complex systems well enough to solve surprisingly difficult problems.

The main challenge in social problem analysis is to figure out what the most important feedback loops driving a system’s behavior are and then what they should be. While large social systems contain millions of loops, the decisive behavior of any specific problem is controlled by only a few of these loops. These determine the basic structure of a system, which is the shape of the important loops defining the system’s behavior of interest. The First Law of Modeling states that if you don’t understand a system’s key feedback loops then you don’t understand the system. But once you do understand them you are a giant step closer to controlling the system’s behavior.

Feedback loops control the behavior of a system over time, as shown by the graphs above. Reinforcing loops cause either runaway (1) exponential growth or (2) exponential decline. For example, the world’s population began growing exponentially when its limits were suddenly raised by the fruits of the Industrial Revolution and modern technology. Balancing loops cause (2) goal-seeking behavior. In a balancing loop a quantity such as temperature will grow rapidly for awhile and then slow down, as it homes in on its goal.

A balancing loop with a delay causes (4) oscillation around the goal of the loop. The loop is continually overshooting and undershooting the goal due to the information delay. It learns too late when it should correct its growth or decline. For example, most thermostats oscillate around their temperature goal by a few degrees.

All realistic models have at least one reinforcing and one balancing loop. An exponential growth loop combined with a goal seeking loop and a small delay causes (5) S-shaped growth with small overshoot. As the delay for correction to the goal grows, the overshoot becomes so large that the perfect approach to the goal shown in graph 5 becomes impossible. The results is (6) S shaped growth with large overshoot and collapse. This is the plight of Homo sapiens today. Due to high systemic change resistance, solution delay has caused large overshoot. The larger the overshoot becomes the more likely a large collapse becomes.

NOTE - The above six graphs were reconstructed from nearly identical ones in John Sterman's Business Dynamics: Systems Thinking and Modeling for a Complex World, page 108. Sterman makes the important point that "The most fundamental modes of behavior are exponential growth, goal seeking, and oscillation." The additional three graphs are merely combinations or variations of the three fundamental modes.

Causal loop diagramming is a much simpler and easier to learn method. The feedback loops in this page are an example of causal loop diagrams. Learning how to draw causal loop diagrams is the place to start if you are learning how to use feedback loop thinking to solve difficult problems.

Simulation modeling is the other method. Here you use software to describe the feedback loops that cause the problem. This creates a simulation model of the problem. Running the model creates graphical output of how the system with the problem behaves over time. For general purpose use, Thwink.org recommends system dynamics as the most appropriate simulation modeling tool for difficult social problems.

Are you as concerned as we are about the rise of populust authoritarians like Donald Trump? Have you noticed that democracy is unable to solve important problems like climate change, war, and poverty? If so this film series is for you!

Why is democracy in crisis? One intermediate cause is a weakened Voter Feedback Loop. Powerful root cause forces are working to weaken the loop.

The most eye-opening article on the site since it was written in December 2005. More people have contacted us about this easy to read paper and the related Dueling Loops videos than anything else on the site.

Do you every wonder why the sustainability problem is so impossibly hard to solve? It's because of the phenomenon of change resistance. The system itself, and not just individual social agents, is strongly resisting change. Why this is so, its root causes, and several potential solutions are presented.

The analysis was performed over a seven year period from 2003 to 2010. The results are summarized in the Summary of Analysis Results, the top of which is shown below:

Click on the table for the full table and a high level discussion of analysis results.

The Universal Causal Chain

This is the solution causal chain present in all problems. Popular approaches to solving the sustainability problem see only what's obvious: the black arrows. This leads to using superficial solutions to push on low leverage points to resolve intermediate causes.

Popular solutions are superficial because they fail to see into the fundamental layer, where the complete causal chain runs to root causes. It's an easy trap to fall into because it intuitively seems that popular solutions like renewable energy and strong regulations should solve the sustainability problem. But they can't, because they don't resolve the root causes.

In the analytical approach, root cause analysis penetrates the fundamental layer to find the well hidden red arrow. Further analysis finds the blue arrow.Fundamental solution elements are then developed to create the green arrow which solves the problem. For more see Causal Chain in the glossary.

This is no different from what the ancient Romans did. It’s a strategy of divide and conquer. Subproblems like these are several orders of magnitude easier to solve because you are no longer trying (in vain) to solve them simultaneously without realizing it. This strategy has changed millions of other problems from insolvable to solvable, so it should work here too.

For example, multiplying 222 times 222 in your head is for most of us impossible. But doing it on paper, decomposing the problem into nine cases of 2 times 2 and then adding up the results, changes the problem from insolvable to solvable.

Change resistance is the tendency for a system to resist change even when a surprisingly large amount of force is applied.

Overcoming change resistance is the crux of the problem, because if the system is resisting change then none of the other subproblems are solvable. Therefore this subproblem must be solved first. Until it is solved, effort to solve the other three subproblems is largely wasted effort.

The root cause of successful change resistance appears to be effective deception in the political powerplace. Too many voters and politicians are being deceived into thinking sustainability is a low priority and need not be solved now.

The high leverage point for resolving the root cause is to raise general ability to detect political deception. We need to inoculate people against deceptive false memes because once people are infected by falsehoods, it’s very hard to change their minds to see the truth.

Life form improper coupling occurs when two social life forms are not working together in harmony.

In the sustainability problem, large for-profit corporations are not cooperating smoothly with people. Instead, too many corporations are dominating political decision making to their own advantage, as shown by their strenuous opposition to solving the environmental sustainability problem.

The root cause appears to be mutually exclusive goals. The goal of the corporate life form is maximization of profits, while the goal of the human life form is optimization of quality of life, for those living and their descendents. These two goals cannot be both achieved in the same system. One side will win and the other side will lose. Guess which side is losing?

The high leverage point for resolving the root cause follows easily. If the root cause is corporations have the wrong goal, then the high leverage point is to reengineer the modern corporation to have the right goal.

The world’s solution model for solving important problems like sustainability, recurring wars, recurring recessions, excessive economic inequality, and institutional poverty has drifted so far it’s unable to solve the problem.

The root cause appears to be low quality of governmental political decisions. Various steps in the decision making process are not working properly, resulting in inability to proactively solve many difficult problems.

This indicates low decision making process maturity. The high leverage point for resolving the root cause is to raise the maturity of the political decision making process.

In the environmental proper coupling subproblem the world’s economic system is improperly coupled to the environment. Environmental impact from economic system growth has exceeded the capacity of the environment to recycle that impact.

This subproblem is what the world sees as the problem to solve. The analysis shows that to be a false assumption, however. The change resistance subproblem must be solved first.

The root cause appears to be high transaction costs for managing common property (like the air we breath). This means that presently there is no way to manage common property efficiently enough to do it sustainably.

The high leverage point for resolving the root cause is to allow new types of social agents (such as new types of corporations) to appear, in order to radically lower transaction costs.

Solutions

There must be a reason popular solutions are not working.

Given the principle that all problems arise from their root causes, the reason popular solutions are not working (after over 40 years of millions of people trying) is popular solutions do not resolve root causes.

This is Thwink.org’s most fundamental insight.

Summary of Solution Elements

Using the results of the analysis as input, 12 solutions elements were developed. Each resolves a specific root cause and thus solves one of the four subproblems, as shown below:

Click on the table for a high level discussion of the solution elements and to learn how you can hit the bullseye.

The 4 Subproblems

The solutions you are about to see differ radically from popular solutions, because each resolves a specific root cause for a single subproblem. The right subproblems were found earlier in the analysis step, which decomposed the one big Gordian Knot of a problem into The Four Subproblems of the Sustainability Problem.

Everything changes with a root cause resolution approach. You are no longer firing away at a target you can’t see. Once the analysis builds a model of the problem and finds the root causes and their high leverage points, solutions are developed to push on the leverage points.

Because each solution is aimed at resolving a specific known root cause, you can't miss. You hit the bullseye every time. It's like shooting at a target ten feet away. The bullseye is the root cause. That's why Root Cause Analysis is so fantastically powerful.

The high leverage point for overcoming change resistance is to raise general ability to detect political deception. We have to somehow make people truth literate so they can’t be fooled so easily by deceptive politicians.

This will not be easy. Overcoming change resistance is the crux of the problem and must be solved first, so it takes nine solution elements to solve this subproblem. The first is the key to it all.

B. How to Achieve Life Form Proper Coupling

In this subproblem the analysis found that two social life forms, large for-profit corporations and people, have conflicting goals. The high leverage point is correctness of goals for artificial life forms. Since the one causing the problem right now is Corporatis profitis, this means we have to reengineer the modern corporation to have the right goal.

Corporations were never designed in a comprehensive manner to serve the people. They evolved. What we have today can be called Corporation 1.0. It serves itself. What we need instead is Corporation 2.0. This life form is designed to serve people rather than itself. Its new role will be that of a trusted servant whose goal is providing the goods and services needed to optimize quality of life for people in a sustainable manner.

What’s drifted too far is the decision making model that governments use to decide what to do. It’s incapable of solving the sustainability problem.

The high leverage point is to greatly improve the maturity of the political decision making process. Like Corporation 1.0, the process was never designed. It evolved. It’s thus not quite what we want.

The solution works like this: Imagine what it would be like if politicians were rated on the quality of their decisions. They would start competing to see who could improve quality of life and the common good the most. That would lead to the most pleasant Race to the Top the world has ever seen.

Presently the world’s economic system is improperly coupled to the environment. The high leverage point is allow new types of social agents to appear to radically reduce the cost of managing the sustainability problem.

This can be done with non-profit stewardship corporations. Each steward would have the goal of sustainably managing some portion of the sustainability problem. Like the way corporations charge prices for their goods and services, stewards would charge fees for ecosystem service use. The income goes to solving the problem.

Corporations gave us the Industrial Revolution. That revolution is incomplete until stewards give us the Sustainability Revolution.

This analyzes the world’s standard political system and explains why it’s operating for the benefit of special interests instead of the common good. Several sample solutions are presented to help get you thwinking.

Note how generic most of the tools/concepts are. They apply to far more than the sustainability problem. Thus the glossary is really The Problem Solver's Guide to Difficult Social System Problems, using the sustainability problem as a running example.